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David L. Andra Jr., Elizabeth M. Quoetone, and William F. Bunting


This paper examines concepts related to warning decision making for the 3 May 1999 tornado outbreak in central Oklahoma. Sixty-six tornadoes occurred during this outbreak, with 58 occurring in the Norman, Oklahoma, National Weather Service Weather Forecast Office (WFO) area of responsibility. Verification statistics for the event revealed the WFO issued 48 tornado warnings, with a median lead time of 23 min, a false-alarm rate of 0.29, and a probability of detection of 0.89. WFO Norman meteorologists utilized a warning decision-making methodology that relied upon 1) scientifically based conceptual models of storm types and their environments, 2) Doppler radar data, 3) ground-truth observations, 4) technology, 5) strategy, and 6) human expertise. This methodology was compared with the ability of radar algorithms [e.g., Weather Surveillance Radar-1988 Doppler (WSR-88D) Mesocyclone (MA) and Tornado Detection Algorithms (TDA)] to identify tornado threat. Although the steady-state nature of the isolated long-lived tornadic supercells presumably presented an ideal case for algorithm performance, shortcomings were identified. The most significant finding was the difference in median lead times between the WFO's subjective human tornado warning and signature detection by TDA for the first tornado associated with each supercell. The first tornado is especially significant because ground truth of the tornado is not yet available and radar signatures are less defined at this early stage. Median lead times were 2 min for TDA and 29 min for the WFO. The MA and TDA proved most useful when used as a safety net or check against the WFO warnings. The initial tornado warning for one supercell storm would have been delayed had the TDA not alerted the meteorologist to investigate the storm.

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Ariel E. Cohen, Richard L. Thompson, Steven M. Cavallo, Roger Edwards, Steven J. Weiss, John A. Hart, Israel L. Jirak, William F. Bunting, Jaret W. Rogers, Steven F. Piltz, Alan E. Gerard, Andrew D. Moore, Daniel J. Cornish, Alexander C. Boothe, and Joel B. Cohen


During the 2014–15 academic year, the National Oceanic and Atmospheric Administration (NOAA) National Weather Service Storm Prediction Center (SPC) and the University of Oklahoma (OU) School of Meteorology jointly created the first SPC-led course at OU focused on connecting traditional theory taught in the academic curriculum with operational meteorology. This class, “Applications of Meteorological Theory to Severe-Thunderstorm Forecasting,” began in 2015. From 2015 through 2017, this spring–semester course has engaged 56 students in theoretical skills and related hands-on weather analysis and forecasting applications, taught by over a dozen meteorologists from the SPC, the NOAA National Severe Storms Laboratory, and the NOAA National Weather Service Forecast Offices. Following introductory material, which addresses many theoretical principles relevant to operational meteorology, numerous presentations and hands-on activities focused on instructors’ areas of expertise are provided to students. Topics include the following: storm-induced perturbation pressure gradients and their enhancement to supercells, tornadogenesis, tropical cyclone tornadoes, severe wind forecasting, surface and upper-air analyses and their interpretation, and forecast decision-making. This collaborative approach has strengthened bonds between meteorologists in operations, research, and academia, while introducing OU meteorology students to the vast array of severe thunderstorm forecast challenges, state-of-the-art operational and research tools, communication of high-impact weather information, and teamwork skills. The methods of collaborative instruction and experiential education have been found to strengthen both operational–academic relationships and students’ appreciation of the intricacies of severe thunderstorm forecasting, as detailed in this article.

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